Web page load time prediction and simulation

ABSTRACT

Embodiments of automated cloud service performance prediction are disclosed. The automated cloud service performance prediction includes extracting one or more dependency relationships for each web object in the webpage. The prediction further includes determining an original performance metric value and original timing information associated with a page loading of a webpage. The prediction also includes simulating a page loading of the webpage based on the adjusted timing information and the dependency relationships to estimate a new performance metric value associated with the simulated page loading of the webpage. The prediction additionally includes comparing the original performance metric value to the new performance metric value to determine whether the adjusted timing information increased or decreased the new performance metric value relative to the original performance metric value.

CROSS REFERENCE TO RELATED APPLICATIONS

This patent application is a divisional application of, and claimspriority to, co-pending, commonly-owned U.S. patent application Ser. No.13/267,254 entitled “WEB PAGE LOAD TIME PREDICTION AND SIMULATION”, andfiled on Oct. 6, 2011, which is a divisional application of U.S. Pat.No. 8,078,691 issued on Dec. 13, 2011, entitled “WEB PAGE LOAD TIMEPREDICTION AND SIMULATION”, these applications are incorporated hereinin their entirety by reference.

BACKGROUND

“Cloud computing” refers to the access of computing resources and datavia a network infrastructure, such as the Internet. Online serviceproviders may offer a wide range of services that may be hosted in thecloud; and such services may include search, maps, email, and outsourcedenterprise applications. Further, online service providers may strive toachieve high levels of end-to-end cloud service performance to sustainand grow their user base. The performance of cloud services has directimpact on user satisfaction. For example, poor end-to-end responsetimes, e.g., long page load times (PLTs), may result in low serviceusage, which in turn may undermine service income.

In order to achieve high levels of end-to-end performance, onlineservice providers may implement performance boosting changes. Forexample, performance boosting changes may include improvements to theclient-side rendering capability of web browsers, improved backendserver-side processing capabilities, and the reduction in the domainname system (DNS) resolution time and/or network round-trip time (RTT).However, cloud computing service implementations are often complex,spanning components on end-system clients, back-end servers, as well asnetwork paths. Thus, performance boosting changes may vary greatly infeasibility, cost and profit generation.

SUMMARY

Described herein are performance prediction techniques for automaticallypredicting the impact of various optimizations on the performance ofcloud services, and systems for implementing such techniques.

The cloud services may include, but are not limited to, web searches,social networking, web-based email, online retailing, online maps,personal health information portals, hosted enterprise applications, andthe like. The cloud services are often rich in features and functionallycomplex. For instance, a sample “driving directions” webpage provided byYahoo! Maps was examined and found to comprise approximately 100 webobjects and several hundred KB of JavaScript code. The web objects maybe retrieved from multiple data centers (DCs) and content distributionnetworks (CDNs). These dispersed web objects may meet only at a clientdevice, where they may be assembled by a browser to form a completewebpage. Moreover, the web objects may have a plethora of dependencies,which means that many web objects cannot be downloaded until some otherweb objects are available. For instance, an image download may have towait for a JavaScript® to execute in an instance where the imagedownload is requested by the download JavaScript. Accordingly,optimizations of the cloud services may include, but are not limited to,modifications of content distribution networks, improvements toclient-side rendering capability of web browsers, improved backendserver-side processing capabilities, and reductions in DNS resolutiontime and/or network round-trip time (RTT).

Thus, because of the complexity of the cloud services, variability ofthe interdependencies between web objects, as well as the differentcombinations of potential optimizations, it is often difficult tounderstand and predict the impact of various optimizations on theperformance of cloud services. However, techniques described herein, andthe implementing systems, may enable performance predictions via one ormore inferences of dependencies between web objects, and the simulateddownload of web objects in a webpage via a web browser. The simulationmay further include the simulated modification of parameters that affectcloud service performance. Such parameters may include, but are notlimited to, round-trip time (RTT), network processing time (e.g., DNSlookup time, TCP handshake time, or data transfer time), clientexecution time, and server processing time. In this way, the techniques,and the implementing systems, described herein may enable the assessmentof cloud service performance under a wide range of hypotheticalscenarios. Thus, the techniques and systems described herein may enablethe prediction of parameter settings that provide optimal cloud serviceperformance, i.e., shortest page load time (PLT).

In at least one embodiment, the automated cloud service performanceprediction includes extracting a parental dependency graph (PDG) for awebpage. The PDG encapsulates one or more dependency relationships foreach web object in the webpage. The prediction further includesdetermining an original page load time (PLT) and original timinginformation of a webpage. The prediction also includes simulating a pageloading of the webpage based on adjusted timing information of each webobject and the PDG to estimate a new PLT of the webpage. The predictionadditionally includes comparing the original PLT of the webpage to thenew PLT of the webpage to determine whether the adjusted timinginformation increased or decreased the new PLT of the webpage. Otherembodiments will become more apparent from the following detaileddescription when taken in conjunction with the accompanying drawings.

This Summary is provided to introduce a selection of concepts in asimplified form that is further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference number in different figures indicates similaror identical items.

FIG. 1 is a block diagram that illustrates an example architecture thatimplements automated performance prediction for cloud services, inaccordance with various embodiments.

FIG. 2 is a block diagram that illustrates selected components forautomatically predicting the performance of cloud services, inaccordance with various embodiments.

FIG. 3 is a block diagram that illustrates the extractions of streamparents and their dependency offsets for web objects, in accordance withvarious embodiments.

FIGS. 4 shows block diagrams that illustrate four scenarios of objecttiming relationship for a single HTTP object, in accordance with variousembodiments.

FIG. 5 is a flow diagram that illustrates an example process to extracta parental dependency graph (PDG) for a webpage, in accordance withvarious embodiments.

FIG. 6 is a flow diagram that illustrates an example process to deriveand compare a new page load time (PLT) of a webpage with an original PLTof the web page, in accordance with various embodiments.

FIG. 7 is a block diagram that illustrates a representative computingdevice that may implement automated performance prediction for cloudservices.

DETAILED DESCRIPTION

This disclosure is directed to the implementation of automatedperformance prediction for cloud services. Cloud service implementationsare often complex, spanning components on end system clients, backendservers, as well as a variety of network paths. In various instances,the performance of the cloud services may be improved, i.e., page loadtime (PLT) minimized, via the modification of parameters that affectcloud service performance. Such parameters may include, but are notlimited to, round-trip time (RTT), network processing time (e.g., DNSlookup time, TCP handshake time, or data transfer time), clientexecution time, and server processing time. However, it is oftendifficult to understand and predict the true effect of parametermodifications on the performance of clouds. This difficulty may beattributed to the complexity of the cloud service implementations,variability of the interdependencies between web objects that access thecloud services, as well as the different options for optimizing accessto cloud services. In fact, manual efforts to assess the impact ofparameter modifications on the performance of cloud service can, inpractice, be overly burdensome and error prone.

In various embodiments, the automated performance prediction for cloudservices may enable the simulated modification of parameters that affectcloud service performance. Thus, the techniques, and the implementingsystems, described herein may enable the prediction of parametersettings that provide optimal cloud service performance, i.e., shortestPLT. Therefore, the use of automated performance prediction for cloudservices may improve PLTs of cloud services without the error proneand/or time consuming manual trial-and-error parameter modifications andperformance assessments.

Further, the data generated by the automated performance predictions maybe used by online service providers to implement parameter changes thatimprove the performance of cloud services without the addition of datacenters, servers, switches, and/or other hardware infrastructure. Thus,user satisfaction with the performance of the cloud services may bemaximized at a minimal cost. Various examples for implementing automatedperformance prediction for cloud services in accordance with theembodiments are described below with reference to FIGS. 1-7.

Example Architecture

FIG. 1 is a block diagram that illustrates an example architecture 100that implements automated performance prediction for cloud services. Inparticular, this architecture 100 implements automated performanceprediction to help improve the end-to-end response times of the cloudservices.

The example architecture 100 may be implemented on a “computing cloud”,which may include a plurality of data centers, such as the data centers102(1)-102(n). As shown in FIG. 1, the data center 102(1) may includeone or more servers 104(1), the data center 102(2) may include one ormore servers 104(2), the data center 102(3) may include one or moreservers 104(3), and the data center 102(n) may include one or moreservers 104(n).

The data centers 102(1)-102(n) may provide computing resource and datastorage/retrieval capabilities. As used herein, computing resources mayrefer to any hardware and/or software that are used to process inputdata to generate output data, such as by the execution of algorithms,programs, and/or applications. Each of the respective data centers maybe further connected to other data centers via the networkinfrastructure 106, such as the Internet. Moreover, the data centers102(1)-102(n) may be further connected to one or more clients, such as aclient 108, via the network infrastructure 106.

The data center 102(1)-102(n) may use their computing resources and datastorage/retrieval capabilities to provide cloud services. These cloudservices may include, but are not limited to, web searches, socialnetworking, web-based email, online retailing, online maps, and thelike. Cloud services may be delivered to users in form of web pages thatcan be rendered by browsers. Sophisticated web pages may containnumerous static and dynamic web objects arranged hierarchically. To loada typical webpage, a browser may first download a main HTML object thatdefines the structure of the webpage. The browser may then download aCascading Style Sheets (CSS) object that describes the presentation ofthe webpage. The main HTML object may be embedded with many JavaScriptobjects that are executed locally to interact with a user. As thewebpage is being rendered, an HTML or a JavaScript object of the webpagemay request additional objects, such as images and additional JavaScriptobjects. This process may continue recursively until all relevantobjects are downloaded.

A cloud service analyzer 110 may be implemented on a client device 108.The client device 108 may be a computing device that is capable ofreceiving, processing, and output data. For example, but not as alimitation, the client device 108 may be a desktop computer, a portablecomputer, a game console, a smart phone, or the like. The cloud serviceanalyzer 110 may predict the impact of various optimizations onuser-perceived performance of cloud services.

In operation, the cloud service analyzer 110 may derive a parentaldependency graph (PDG) 112 for a particular webpage. As furtherdescribed below, the PDG 112 may represent the various web objectdependencies that may be present in a webpage. The dependencies betweenweb objects may be caused by a number of reasons. The common ones mayinclude, but is not limited to: (1) the embedded web objects in an HTMLpage depend on the HTML page; (2) since at least some web objects aredynamically requested during JavaScript execution, these web objects maydepend on the corresponding JavaScript; (3) the download of an externalCSS or JavaScript object may block the download of other types of webobjects in the same HTML page; and (4) web object downloads may dependon certain events in a JavaScript or the browser.

The cloud service analyzer 110 may further obtain a total page load time(PLT) 114 of the webpage 116 by performing a page load in a baselinescenario. During the page load, the cloud service analyzer 110 may alsoobtain parameters, i.e., timing information 118, related to the downloadof each web object in the webpage 116. The timing information 118 mayinclude client delay, network delay, and server delay that occur duringthe page load of the webpage. For example, the client delay may be dueto various browser activities such as page rendering and JavaScriptexecution. The network delay may be due to DNS lookup time, transmissioncontrol protocol (TCP) handshake time, and data transfer time. Both TCPhandshake time and data transfer time may be influenced by network pathconditions such as RTT and packet loss. Moreover, the server delay maybe incurred by various server processing tasks such as retrieving staticcontent and generating dynamic content.

Having obtained the timing information 118 for each web object in thewebpage 116 for the baseline scenario, the cloud service analyzer 110may add additional client delay 120 for each web object to the timinginformation 118. In at least one embodiment, the cloud service analyzer110 may infer the additional client delay by combining the timinginformation 118 with the PDG 112 of the webpage 116.

Subsequently, the cloud service analyzer 110 may receive a new scenariothat includes modified parameters, i.e., modified timing information122. For example, but not as a limitation, the cloud service analyzer110 may receive modifications to RTT, modifications to networkprocessing time, modifications to client execution time, modificationsto server processing time, and/or the like. The cloud service analyzer110 may simulate the page loading of all web objects of the webpage 116based on the modified time information 122 and the PDG 112 to estimatethe second PLT 124 of the webpage. Subsequently, the cloud serviceanalyzer 110 may compare the second PLT 124 of the webpage to the firstPLT 114 of the webpage to determine whether the load time has beenimproved via the parameters modifications (e.g., whether a shorter loadtime is achieved). Thus, by repeating the modifications of theparameters and the comparisons, the cloud service analyzer 110 may testa plurality of new scenarios to derive improved parameter settings thatminimize PLT.

Example Components

FIG. 2 is a block diagram that illustrates selected components forperforming automated performance prediction for cloud services. Theselected components may be implemented on a client device 108 (FIG. 1).The client device 108 may include one or more processors 202 and memory204.

The memory 204 may include volatile and/or nonvolatile memory, removableand/or non-removable media implemented in any method or technology forstorage of information, such as computer-readable instructions, datastructures, program modules or other data. Such memory may include, butis not limited to, random accessory memory (RAM), read-only memory(ROM), electrically erasable programmable read-only memory (EEPROM),flash memory or other memory technology, CD-ROM, digital versatile disks(DVD) or other optical storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, RAID storagesystems, or any other medium which can be used to store the desiredinformation and is accessible by a computer system. Further, thecomponents may be in the form of routines, programs, objects, and datastructures that cause the performance of particular tasks or implementparticular abstract data types.

The memory 204 may store components. The components, or modules, mayinclude routines, programs instructions, objects, and/or data structuresthat perform particular tasks or implement particular abstract datatypes. The selected components may include a measurement engine 206, adependency extractor 222, and a performance predictor 228, a comparisonengine 212, a data storage module 238, a comparison engine 240, and auser interface module 242. The various components may be part of thecloud server analyzer 110 (FIG. 1).

Measurement Engine

The measurement engine 206 may collect packet traces 216 that enable thecloud service analyzer 102 to determine the page load times (PLTs) ofweb pages, as well as determine DNS, TCP handshake, and HTTP objectinformation. The measurement engine 206 may include a set of measurementagents 208 located at different network nodes and a central controller210. The controller 210 may use an application updater 212 to upgradethe script snippets that simulate user interaction with particularwebsites and workload (e.g., the input to cloud services). Moreover, thecontroller 210 may store packet traces 216 collected from the differentagents 208 by a data collector 214.

The measurement engine 206 may run an automated web robot 218 thatdrives a full-featured web browser. The web robot 218 may be used tosimulate user inputs and to request web pages. Further, the web robot218 may call a trace logger 220 to log the packet traces 216 multipletimes so that the measurement engine 206 may recover the distributionalproperties of a particular page loading process. In various embodiments,the web robot 218 may also take control of a web browser via a browserplug-in. The web robot 218 may include a script library that enables theweb robot to control browser related functions. These functions mayinclude cache control, browser parameter adjustment, as well as userinput simulation.

Dependency Extractor

The dependency extractor 222 may include an algorithm to inferdependencies between web objects by perturbing the download times ofindividual web objects. The algorithm may leverage the fact that thedelay of an individual web object will be propagated to all otherdependent web objects. Accordingly, this time perturbation may besystematically applied to discover web object dependencies.

Modern web pages may contain many types of web objects such as HTML,JavaScript, CSS, and images. These embedded web objects may bedownloaded recursively instead of all at once. For instance, the mainHTML may contain a JavaScript whose execution will lead to additionaldownloads of HTML and image objects. Thus, a web object may beclassified as being dependent on another if the former cannot bedownloaded until the latter is available. The dependencies between webobjects can be caused by a number of reasons. The common reasons mayinclude, but are not limited to: (1) the embedded web objects in an HTMLpage may depend on the HTML page; (2) since web objects are dynamicallyrequested during JavaScript execution, these web objects may depend onthe corresponding JavaScript; (3) the download of an external CSS orJavaScript object may block the download of other types of objects inthe same HTML page; and (4) web object downloads may depend on certainevents in a JavaScript or the browser. For instance, a JavaScript maydownload image B only after image A is loaded. In other words, given animage A, the dependent web objects of image A usually cannot berequested before image A is completely downloaded.

However, there are exceptions to the common dependencies betweenobjects, and therefore the following may provide context for thefunctionality of the dependency extractor 222. In some instances, abrowser may render an HTML page in a streamlined fashion, in which theHTML page may be partially displayed before the downloading finishes.For instance, if an HTML page has an embedded image with tag <img>, theimage may be downloaded and displayed in parallel with the downloadingof the HTML page. In fact, the image download may start once the tag<img> (identified by a byte offset in the HTML page) has been parsed.Thus, web objects that can be processed in a streamlined fashion may bedefined as stream objects, and all HTML objects may be stream objects.For instance, given a stream object A, the notation dependent offset_(A)(img) may be used to denote the byte offset of <img> in the streamobject A. In one example, browsers that exhibit streamlined processingbehavior may include the Internet Explorer® browser produced by theMicrosoft® Corporation of Redmond, Wash.

Thus, in order to distinguish stream objects from non-stream objects, adifferent kind of notation may be used to denote dependencies betweennon-stream objects. In various embodiments, given a non-stream object Xthe notation descendant (X) may be used to denote the set of objectsthat depend on the non-stream object X. Conversely, the notationancestor (X) may be used to denote the set of web objects that thenon-stream object X depends on. Thus, by definition, the non-streamobject X cannot be requested until all the objects in ancestor (X) areavailable.

Among the objects in ancestor (X), the object whose loading immediatelyprecedes the loading of the non-stream object X may be denoted as thelast parent of the non-stream object X Thus, if such a preceding webobject is designated as object Y, the available-time of the object Y maybe expressed as when the dependency offset_(Y) (X) has been loaded. Inembodiments where object Y is a non-stream object, the available-time ofthe object Y may be when the object Y is completed loaded. As furtherdescribed below, the available-time of the object Y may be used toestimate the start time of the non-stream object X's download andpredict the page load time (PLT) of a webpage. Further, while thenon-stream object X has only one last parent, the last parent of thenon-stream object X may change across different page loads due tovariations in the available-time of its ancestors. Accordingly, parent(X) may be used denote the object in ancestor (X) that may be the lastparent of the non-stream object X.

Therefore, as further described below, given a webpage, a parentaldependency graph (PDG) 112 (FIG. 1) may be used to encapsulate theparental relationships between web objects in the webpage. For example,a PDG=(V,E) may be a directed acyclic graph (DAG) that includes a set ofnodes and directed links, where each node is a web object. Moreover,each link Y→X means Y is a parent of X. Thus, the work performed by thedependency extractor 222 may be explained based on the above describednotations and relationships.

The dependency extractor 222 may extract the dependencies of a webpageby perturbing the download of individual web objects. The dependencyextractor 222 may leverage the fact that the delay of an individual webobject may be propagated to all other dependent web objects.Accordingly, the dependency extractor 222 may extract the stream parentof a web object and the corresponding dependent offset. For example,suppose a web object X has a stream parent Y, to discover this parentalrelationship, the available time of Offset_(Y) (X) should be earlierthan all of the other parents of the web object X in a particular pageload. Thus, the dependency extractor 222 may control the download of notonly each non-stream parent of the web object X as a whole, but alsoeach partial download of each stream parent of the web object X

In order to extract dependencies of the web objects of a web page, thedependency extractor 222 may discover ancestors and descendants for theweb objects. In various embodiments, the dependency extractor 222 mayuse a web proxy 224 to delay web object downloads of a webpage andextract the list of web objects in the webpage by obtaining theircorresponding Universal Resource Identifiers (URIs). Further, thedependency extractor 222 may discover the descendants of each web objectusing an iterative process. In various embodiments, the dependencyextractor 222 may reload the page and delay the download of a webobject, such as web object X, for τ seconds in each round of theiterative process. In each round, the web object X may be a web objectthat has not been processed, and T may be greater than the normalloading time of the web object X. The descendants of the web object Xmay be web objects whose download is delayed together with the webobject X The iterative process may be repeated until the descendants ofall the web objects are discovered. During the iterative process, thedependency extractor 222 may use a browser robot 226 to reload pages. Insuch embodiments, the operation of the dependency extractor may be basedon two assumptions: (1) the dependencies of a webpage do not changeduring the discovery process; and (2) introduced delays will not changethe dependencies in the webpage.

The dependency extractor 222 may also extract non-stream parents of eachweb object during the process of extracting dependencies of the webobjects. In various embodiments, given a non-stream web object X and itsweb object Z, non-stream web object X may be the parent of descendantweb object Z if and only if there does not exist a web object Y that isalso the descendant of web object X and the ancestor of descendant webobject Z. On the other hand, if such web object Y exists, theavailable-time of the web object Y may be later than that of thenon-stream web object X This is because the web object Y cannot bedownloaded until the non-stream web object X is available, which impliesthe web object X cannot be the parent of the web object Z. However, ifthe web object Y does not exist, there may be scenarios in which thenon-stream web object X is delayed until all of the other ancestors ofthe web object Z are available. This may be possible because none of theother ancestors of the web object Z depend on the web object X, whichmay imply that the non-stream web object X may indeed be the parent ofthe descendant web object Z. Based on these observations, the dependencyextractor 222 may use the following algorithm (shown in pseudo code) totake a set of web objects and the set of descendants of each web objectas input and compute the parent set of each web object:

ExtractNonStreamParent(Object, Descendant) For X in Object For Z inDescendant (X) IsParent = True For Y in Descendant (X) If (Z inDescendant (Y) ) IsParent = False Break EndIf EndFor If (Isparent) add Xto Parent (Z) EndFor EndFor

Furthermore, the dependency extractor 222 may also extract streamparents and their dependency offsets. The extractions of stream parentsand their dependency offsets are illustrated in FIG. 3. As shown in FIG.3, a HTML stream object H 302 may contain a JavaScript J 304 and animage object I 306. JavaScript J 304 and image object I 306 may beembedded in the beginning and the end of stream object H 302respectively (offset_(H) (J)<offset_(H) (I)). However, because the URLof image object I 306 is defined in JavaScript J 304, image object I 306cannot be downloaded until JavaScript J is executed. This causes theimage object I 306 to depend on both the stream object H 302 and theJavaScript J 304 while the JavaScript J 304 only depend on the streamobject H 302.

In a normal scenario, the stream object H 302 cannot be the parent ofthe image object I 306 since the JavaScript object J 304 is thedescendant of stream object H 302 and the ancestor of the image object I306. Nonetheless, when the download of the stream object H 302 is slow,the JavaScript J 304 may be downloaded and executed before the offs etHO becomes available. In such a case, the stream object H 302 may becomethe last parent of the image object I 306.

Thus, given the stream object H 302 and its descendant image object I306, the dependency extractor 222 may determine whether the streamobject H 302 is the parent of the image object I 306. In variousembodiments, the dependency extractor 222 may first load an entirewebpage and control the download of the stream object H 302 at anextremely low rate λ. If the stream object H 302 is the parent of theimage object I 306, all of the other ancestors of the image object I 306may be available by the time offset_(H) (I) is available. The dependencyextractor 222 may then estimate offset_(H) (I) with offset_(H) (I)′,whereby the latter is the offset of stream object H 302 that has beendownloaded when the request of the image object I starts to be sent out.In some embodiments, offset_(H) (I)′ may be directly inferred fromnetwork traces and is usually larger than offset_(H) (I). This isbecause it may take some extra time to request image object I afteroffset_(H) (I) is available. However, since stream object H 302 isdownloaded at an extremely low rate, these two offsets should be veryclose.

Having inferred offset_(H) (I)′, the dependency extractor 222 mayperform an additional parental test to determine whether stream object H302 is the parent of image object I 306. During the testing process, thedependency extractor 222 may reload the entire webpage. However, forthis reload, the dependency extractor 222 may control the download ofstream object H 302 at the same low rate λ as well as delay the downloadof all the known non-stream parents of image object I 306 by τ. Further,assuming offset_(H) (I)″ is the offset of stream object H 302 that wasdownloaded when the request of image object I 306 is sent out during thereload, if offset_(H) (I)″−offset_(H) (I)′<<τ×λ is true, then the delayof I's known parents has little effect on when image object I 306 isrequested. Accordingly, stream object H 302 may be designated as thelast parent of image object I 306. In this way, the parentalrelationship between the stream objects in the webpage, as well as theirdependency offsets, may be further encapsulated by the PDG 112 (FIG. 1).

The choice of λ may reflect a tradeoff between measurement accuracy andefficiency. A small λ may enable the dependency extractor 222 toestimate offset_(H) (I) more accurately but may also lead to arelatively long processing time, as the parameter T may directly affectthe accuracy of the parental tests. For example, if T is too small, theresults may be susceptible to noise, increasing the chance of missingthe true stream parents. However, if τ is too large, the dependencyextractor 222 may mistakenly infer a parent relationship becauseoffset_(H) (I)″−offset_(H) (I)′ is bound by size_(H)−offset_(H) (I)where size_(H) is the page size of stream object H 302. In at least oneembodiment, the dependency extractor 222 may use λ=size_(H)/200bytes/sec and τ=2 seconds. This means the stream object H takes 200seconds to transfer. However, additional embodiments may use othervalues for λ and τ provided they supply adequate tradeoffs betweenmeasurement accuracy and efficiency.

Performance Predictor

The performance predictor 228 may predict the performance of cloudservices under hypothetical scenarios. Given the page load time (PLT) ofa baseline scenario, the performance predictor 228 may predict the newPLT when there are changes to a plurality of parameters. In variousembodiments, the changes to the parameters may include changes in theclient delay, the server delay, the network delay (e.g., DNS lookuptime, TCP handshake time, or data transfer time), and/or the RTT. Thus,the performance predictor 228 may help service providers identifyperformance bottlenecks and devise effective optimization strategies. Inother words, the performance predictor 228 may simulate page loadingprocesses of a browser under a wide range of hypothetical scenarios.

During the operation of the performance predictor 228, the performancepredictor 228 may first infer the timing information of each web objectfrom the network trace of a webpage load in a baseline scenario. Invarious embodiments, the performance predictor 228 may first obtain thetiming information from the measurement engine 206. Second, based on theparental dependency graph (PDG) of the webpage, the performancepredictor 228 may further annotate each web object with additionalinformation related to client delay. Third, the performance predictor228 may adjust the web object timing information to reflect changes fromthe baseline scenario to the new one. Finally, the performance predictor228 may simulate the page load process with new web object timinginformation to estimate the new page load PLT. Except for the factorsadjusted, the page load process of the new scenario may inheritproperties from the baseline scenario, including non-deterministicfactors, such as whether a domain name is cached by the browser.

Performance Predictor—Obtain Timing Information

The performance predictor 228 may infer web objects and their timinginformation from network traces of a page load collected at the client,such as the client 108 (FIG. 1). In various embodiments, the performancepredictor 228 may include a trace analyzer 230 that recovers DNS, TCPand HTTP object information. For HTTP protocol, the performancepredictor 228 may use TCP reassembly and HTTP protocol parsing torecover the HTTP object information. For DNS protocol, the traceanalyzer 230 may use a DNS parser to recover the domain name related tothe DNS requests and responses. In at least one embodiment, theperformance predictor 228 may leverage a network intrusion detectiontool component to recover the timing and semantic information from theraw packet trace.

The trace analyzer 230 may estimate the RTT of each connection by thetime between SYN and SYN/ACK packets. Based on the TCP self-clockingbehavior, the trace analyzer 230 may estimate the number of round tripsof the HTTP transfer taken by a web object. The packets in one TCPsending window are relatively close to each other (e.g., less than oneRTT), but the packets in different sending windows are about one RTTapart (approximately 1±0.25 RTT). This approach may help the traceanalyzer 230 to identify the additional delay added by the web servers,which is usually quite different from one RTT.

Thus, the performance predictor 228 may be client-deployable since itdoes not require any application-level instrumentations. By using thetrace analyzer 230, the performance predictor 228 may identify objecttiming information 232 for each web object. The timing information 232may include information for three types of activity: (1) DNS lookuptime: the time used for looking up a domain name; (2) TCP connectiontime: the time used for establishing a TCP connection; and (3) HTTPtime: the time used to load a web object.

Moreover, HTTP time may be further decomposed into three parts: (i)request transfer time: the time to transfer the first byte to the lastbyte of the HTTP request; (ii) response time: the time from when thelast byte of the HTTP request is sent to when the first byte of the HTTPreply is received, which may include one RTT plus server delay; and(iii) reply transfer time: the time to transfer the first byte to thelast byte of an HTTP reply.

In addition, the performance predictor 228 may infer the RTT for eachTCP connection. The RTT of a TCP connection may be quite stable sincethe entire page load process usually lasts for only a few seconds. Theperformance predictor 228 may also infer the number of round-tripsinvolved in transferring an HTTP request or reply. Such information mayenable the performance predictor 228 to predict transfer times when RTTchanges.

Performance Predictor—Additional Client Delay Information

As stated above, the performance predictor 228 may further annotate eachobject with additional timing information related to client delay. Invarious embodiments, the timing information 232 may be supplemented withthe additional timing information related to client delay. For example,when the last parent of a web object X becomes available, the browsermay not issue a request for the web object X immediately. This isbecause the browser may need time to perform some additional processing,e.g., parsing HTML pages or executing JavaScripts. For the web object X,the performance predictor 228 may use client delay to denote the timefrom when its last parent is available to when browser starts to requestthe web object X.

When the browser loads a sophisticated webpage or the client machine isslow, client delay may have significant impact on PLT. Accordingly, theperformance predictor 228 may infer client delay of each web object bycombining the obtained object timing information of each web object withthe PDG (e.g., PDG 112) of the webpage. It will be appreciated that whena browser starts to request a web object, the first activity may be DNS,TCP connection, or HTTP, depending on the current state and behavior ofthe browser.

Many browsers may limit the maximum number of TCP connections to a hostserver. For example, some browsers (e.g., Internet Explorer® 7) maylimit the number of TCP connections to six by default. Such limitationsmay cause the request for a web object to wait for available connectionseven when the request is ready to be sent. Therefore, the client delaythat the performance predictor 228 observes in a trace may be longerthan the actual browser processing time. To overcome this problem, whencollecting the packet traces in the baseline scenario for the purpose ofprediction, the performance predictor 228 may set the connection limitper host of the browser to a large number, for instance, 30. This mayhelp to reduce the effects of connection waiting time.

Performance Predictor—Adjust Object Timing Information

Further, having obtained the web object timing information 232 under thebaseline scenario, the performance predictor 228 may adjust the timinginformation 232 for each web object according to a new scenario. Inother words, the timing information 232 may be adjusted with new inputparameters 122 (FIG. 1) that modify one or more of the server delay, theclient delay, the network delay, the RTT time, etc. The new inputparameters 122 (FIG. 1) may be inputted by a user or a testingapplication. The modifications may increase or decrease the value ofeach delay or time.

In at least one embodiment, assuming server_(δ) is the server delaydifference between the new and the baseline scenario, the performancepredictor 228 may add the input server_(δ) to the response time of eachweb object to reflect the server delay change in the new scenario. Theperformance predictor 228 may use similar methods to adjust DNS activityand client delay for each web object with the new scenario inputs 236.

The performance predictor 228 may also adjust for RTT changes. Assumingthe HTTP request and response transfers involve m and n round-trips forthe web object X, the performance predictor 228 may add (m+n+1)×rtt_(δ)to the HTTP activity of the web object X, and rtt_(δ) to the TCPconnection activity if a new TCP connection is required for loading theweb object X. In such embodiments, the performance predictor 228 mayoperate under the assumption that RTT change has little impact on thenumber of round-trips involved in loading a web object.

Performance Predictor—Simulating Page Load

The performance predictor 228 may further predict page load time (PLT)based on the web object timing information 232 by simulating a page loadprocess. Since web object downloads are not independent of each other,the download of a web object may be blocked because its dependentobjects are unavailable or because there are no TCP connections readyfor use. Accordingly, the performance predictor 228 may account forthese limitations when simulating the page load process by taking intoaccounts the constraints of a browser and the corresponding PDG (e.g.,PDG 112) of the webpage.

For example, web browsers such as Internet Explorer® and Firefox®, asproduced by the Mozilla Corporation of Mountain View, Calif., may sharesome common constraints and features. Presently, Internet Explorer® andFirefox® both use HTTP/1.1 with HTTP pipelining disabled by default.This may be due to the fact that HTTP pipelining may perform badly withthe presence of dynamic content, e.g., one slow request may block otherrequests. However, without pipelining, HTTP request-reply pairs do notoverlap with each other within the same TCP connection. However, inHTTP/1.1, a browser may use persistent TCP connections that can bereused for multiple HTTP requests and replies. The browser may attemptto keep the number of parallel connections small. Accordingly, thebrowser may open a new connection only when it needs to send a requestand all existing connections are occupied by other requests or replies.The browser may be further configured with an internal parameter tolimit the maximum number of parallel connections with a particular host.Such limit may be commonly applied to a host instead of to an IPaddress. However, if multiple hosts map to the same IP address, thenumber of parallel connections with that IP address may exceed thelimit. Thus, during the page load simulation, the performance predictor228 may set the number of possible parallel connections according to theconnection limitations of a particular browser. However, in otherembodiments, the number of possible parallel connections may be modified(e.g., increased or decreased). Thus, the new input parameters 122(FIG. 1) of a new scenario may further include the number of possibleparallel connections.

Moreover, the web browsers, such as Internet Explorer® and Firefox®, mayalso share some common features. For example, loading a web object in aweb browser may trigger multiple activities including looking up a DNSname, establishing a new TCP connection, waiting for an existing TCPconnection, and/or issuing an HTTP request. Five possible combinationsof these activities are illustrated below in Table I. A “−” in Table Imeans that the corresponding condition does not matter for the purposeof predicting page load time (PLT). The activities involved in each caseare further illustrated in FIG. 4.

TABLE I Connection Packing Possibilities for a Given Domain Name Case III III IV V First Web Object of a Domain Yes Yes No No No Cached DNSName No Yes — — — Available TCP Connections — — Yes No No Maximum Numberof Parallel — — — No Yes Connections Corresponding Scenario in FIG. 4402 404 406 404 408

FIG. 4 shows block diagrams that illustrate scenarios 402-408 of webobject timing relationship for a single HTTP object, in accordance withvarious embodiments. For instance, in Case V, a browser may load a webobject from a domain with which it already has established TCPconnections. However, because all the existing TCP connections areoccupied and the number of parallel connections has reached a maximumlimit, the browser may have to wait for the availability of an existingconnection to issue the new HTTP request. Case V is illustrated asscenario 408 of FIG. 4.

Based on the common limitations and features of web browsers, theperformance predictor 228 may estimate a new PLT of a webpage by usingprediction engine 234 to simulate the corresponding page load process.In various embodiments, the prediction engine 234 may use an algorithmthat takes the adjusted timing information of each web object in the webpage, as well as the parental dependency graph (PDG) of the webpage asinputs, and simulate the page loading process for the webpage todetermine a new page load time (PLT). The algorithm may be expressed inpseudo code (“PredictPLT”) as follows:

PredictPLT(ObjectTimingInfo, PDG) Insert root objects into CandidateQWhile (CandidateQ not empty) 1) Get earliest candidate C from CandidateQ2) Load C according to conditions in Table 1 3) Find new candidateswhose parents are available 4) Adjust timings of new candidates 5)Insert new candidates into CandidateQ Endwhile

In other words, the PLT may be estimated by the algorithm as the timewhen all the web objects are loaded. For each web object X, thealgorithm may keep track of four time variables: (1) T_(p): when the webobject X's last parent is available; (2) T_(r): when the HTTP requestfor the web object X is ready to be sent; (3) T_(f): when the first byteof the HTTP request is sent; and (4) T_(l): when the last byte of theHTTP reply is received. As stated above, FIG. 4 illustrates the positionof these time variables in four different scenarios. In addition, thealgorithm may maintain a priority queue CandidateQ that contains the webobjects that can be requested. The objects in CandidateQ may be sortedbased on T_(r).

Initially, the algorithm may insert the root objects of thecorresponding PDG of the webpage into the queue CandidateQ with theirT_(r) set to 0. A webpage may have one main HTML object that serves asthe root of the PDG. However, in some scenarios, multiple root objectsmay exist due to asynchronous JavaScript and Extensible Markup Language(XML) downloading, such as via AJAX. In each Iteration, the algorithmmay perform the following tasks: (1) obtain object C with smallestT_(r), from CandidateQ; (2) load object C according to the conditionslisted in Table 1; (2) update T_(f) and T_(l) based on whether theloading involves looking up DNS names, opening new TCP connections,waiting for existing connections, and/or issuing HTTP requests. T_(f)and T_(l) may be used to determine when a TCP connection is occupied bythe object C; (3) after the object C is loaded, find all the newcandidates whose last parent is the object C; (4) adjust the T_(p) andT_(r) of each new candidate X If the object C is a non-stream object,T_(p) of the object X may be set to T_(l) of the object C. However, ifobject C is a stream object, T_(p) of the object X may be set to theavailable-time of offset_(C) (X), and T_(r) of the object X may be setto T_(p), plus the client delay of the object X; and (5) insert newcandidates into CandidateQ.

With the use of such an algorithm and the prediction engine 234, theperformance predictor 228 may simulate a page load process for thewebpage based on the adjusted web object timing information 232, inwhich the adjusted web object timing information 232 includes the newmodified input parameters 122 (FIG. 1). The simulation of the page loadprocess may generate a new predicted PLT for the webpage. In variousembodiments, the performance predictor 228 may output the predicted PLTas the result 236.

The data storage module 238 may be configured to store data in a portionof memory 204 (e.g., a database). In various embodiments, the datastorage module 238 may store web pages for analysis, algorithms used bythe dependency extractor 222 and the prediction engine 234, theextracted PDGs, and timing information of the web objects (e.g., theclient delay, the network delay, and the server delay). The data storagemodule 238 may further store parameter settings for different pageloading scenarios, and/or results such as the PLTs of web pages underdifferent scenarios. The data storage module 238 may also store anyadditional data derived during the automated performance of cloudservices, such as, but not limited, trace information produced by thetrace analyzer 230.

The comparison engine 240 may compare the new predicted PLT (e.g.,result 236) of each webpage with the original PLT of each webpage, asmeasured by the measurement engine 206. Additionally, in someembodiments, the results 236 may also include one or more of the serverdelay, the client delay, the network delay, the RTT, etc., as obtainedduring the simulated page loading of the webpage by the performancepredictor 228. Accordingly, the comparison engine 240 may also comparesuch values to one or more corresponding values obtained by themeasurement engine 206 during the original page load of the webpage.Further, the comparison engine 240 may also compare a plurality ofpredicted PLTs of each webpage, as derived from different sets of inputparameters, so that one or more improved sets of input parameters may beidentified.

The user interface module 242 may interact with a user via a userinterface (not shown). The user interface may include a data outputdevice such as a display, and one or more data input devices. The datainput devices may include, but are not limited to, combinations of oneor more of keypads, keyboards, mouse devices, touch screens,microphones, speech recognition packages, and any other suitable devicesor other electronic/software selection methods. The user interfacemodule 242 may enable a user to input or modify parameter settings fordifferent page loading scenarios, as well as select web pages and pageloading processes for analysis. Additionally, the user interface module242 may further cause the display to output representation of the PDGsof selected web pages, current parameter settings, timing information ofthe web objects, PLTs of web pages under different scenarios, and/orother pertinent data.

Example Processes

FIGS. 5-6 describe various example processes for automated cloud serviceperformance prediction. The order in which the operations are describedin each example process is not intended to be construed as a limitation,and any number of the described blocks can be combined in any orderand/or in parallel to implement each process. Moreover, the blocks inthe FIGS. 5-6 may be operations that can be implemented in hardware,software, and a combination thereof. In the context of software, theblocks represent computer-executable instructions that, when executed byone or more processors, cause one or more processors to perform therecited operations. Generally, computer-executable instructions includeroutines, programs, objects, components, data structures, and the likethat cause the particular functions to be performed or particularabstract data types to be implemented.

FIG. 5 is a flow diagram that illustrates an example process 500 toextract a parental dependency graph (PDG) for a webpage, in accordancewith various embodiments.

At block 502, the dependency extractor 222 of the cloud service analyzer206 may extract a list of web objects in a webpage. In variousembodiments, the dependency extractor 222 may use a web proxy 224 todelay web object downloads of a webpage and extract the list of webobjects in the webpage by obtaining their corresponding UniversalResource Identifiers (URIs).

At block 504, the dependency extractor 222 may load the webpage anddelay the download of a web object so that one or more descendantsand/or one or more ancestors thereof may be discovered. In variousembodiments, the dependency extractor 222 may use a browser robot 226 toload the webpage.

At block 506, the dependency extractor 222 may use an iterativealgorithm to discover all the descendants and the ancestors of the webobject. In various embodiments, the algorithm may extract one or morestream parent objects and/or one or more non-stream parent objects, oneor more descendant objects, as well as the dependency relationshipsbetween the web object and the other web objects.

At block 508, the dependency extractor 222 may encapsulate the one ormore dependency relationships of the web object that is currently beingprocessed in a PDG, such as the PDG 112 (FIG. 1).

At decision block 510, the dependency extractor 222 may determinewhether all the web objects of the webpage have been processed. Invarious embodiments, the dependency extractor 222 may make thisdetermination based on the list of the web objects in the webpage. Ifthe dependency extractor 222 determines that not all of the web objectsin the webpage has been processed (“no” at decision block 510), theprocess 500 may loop back to block 504, where another web object in thelist of web objects may be processed for descendants and ancestor webobjects. However, if the dependency extractor 222 determines all webobjects have been processed (“yes” at decision block 510), the processmay terminate at block 512.

FIG. 6 is a flow diagram that illustrates an example process 600 toderive and compare a new page load time (PLT) of a webpage with anoriginal PLT of the web page, in accordance with various embodiments.

At block 602, the performance predictor 228 of the cloud serviceanalyzer 206 may obtain an original PLT (e.g., PLT 114) of a webpage. Invarious embodiments, the performance predictor 228 may obtain the PLT ofthe webpage from the measurement engine 206.

At block 604, the performance predictor 228 may extract timinginformation (e.g., timing information 232) of each web object using anetwork trace. The network trace may be implemented on a page load ofthe webpage under a first scenario. In various embodiments, theperformance predictor 228 may use trace logger 220 of the measurementengine 206 to perform the network tracing. The first scenario mayinclude a set of default parameters that represent client delay, thenetwork delay (e.g., DNS lookup time, TCP handshake time, or datatransfer time), the server delay, and/or the RTT experienced during thepage load.

At block 606, the performance predictor 228 may annotate each web objectwith additional client delay information based on the parentaldependency graph (PDG) of the webpage. In various embodiments, theadditional client delay information may represent the time a browserneeds to do some additional processing, e.g., parsing HTML pages orexecuting JavaScripts, the time expended due to browser limitation onthe maximum number of simultaneous TCP connections.

At block 608, the performance predictor 228 may adjust the web objecttiming information of each web object to reflect a second scenario thatincludes one or more modified parameters (e.g., modified parameters122). In various embodiments, the modified parameters may have beenmodified based on input from a user or a testing application. Forexample, but not as a limitation, the cloud service analyzer 110(FIG. 1) may receive one or more modifications to RTT, modifications tonetwork processing delay (e.g., modifications to DNS lookup time, TCPhandshake time, or data transfer time), modifications to clientprocessing delay, modifications to server processing delay, and/or thelike. The modifications may increase or decrease the value of each delayor time.

At block 610, the performance predictor 228 may simulate page loading ofthe webpage to estimate a new PLT (e.g., PLT 124) of the webpage. Invarious embodiments, the performance engine may use the predictionengine 234 to simulate the page loading of each web object of thewebpage based on the adjusted time information that includes themodified parameters (e.g., modified parameters 122) and the PDG (e.g.,PDG 112).

At block 612, the comparison engine 240 of the cloud service analyzer206 may compare the new PLT of the webpage to the original PLT of thewebpage to determine whether the load time has been improved via theparameter modifications (e.g., whether a shorter or improved PLT isachieved, or whether the parameter modifications actually increased thePLT). In further embodiments, the comparison engine 240 may also comparea plurality of new PLTs that are derived based on different modifiedtiming information so that improved parameters may be determined.

Example Computing Device

FIG. 7 illustrates a representative computing device 700 that mayimplement automated performance prediction for cloud services. Forexample, the computing device 700 may be a server, such as one of theservers 102(1)-102(n), as described in FIG. 1. Moreover, the computingdevice 700 may also act as the client device 108 described in thediscussion accompanying FIG. 1. However, it will be readily appreciatedthat the techniques and mechanisms may be implemented in other computingdevices, systems, and environments. The computing device 700 shown inFIG. 7 is only one example of a computing device and is not intended tosuggest any limitation as to the scope of use or functionality of thecomputer and network architectures.

In at least one configuration, computing device 700 typically includesat least one processing unit 702 and system memory 704. Depending on theexact configuration and type of computing device, system memory 704 maybe volatile (such as RAM), non-volatile (such as ROM, flash memory,etc.) or some combination thereof. System memory 704 may include anoperating system 706, one or more program modules 708, and may includeprogram data 710. The operating system 706 includes a component-basedframework 712 that supports components (including properties andevents), objects, inheritance, polymorphism, reflection, and provides anobject-oriented component-based application programming interface (API),such as, but by no means limited to, that of the .NET™ Frameworkmanufactured by the Microsoft® Corporation, Redmond, Wash. The computingdevice 700 is of a very basic configuration demarcated by a dashed line714. Again, a terminal may have fewer components but may interact with acomputing device that may have such a basic configuration.

Computing device 700 may have additional features or functionality. Forexample, computing device 700 may also include additional data storagedevices (removable and/or non-removable) such as, for example, magneticdisks, optical disks, or tape. Such additional storage is illustrated inFIG. 7 by removable storage 716 and non-removable storage 718. Computerstorage media may include volatile and nonvolatile, removable andnon-removable media implemented in any method or technology for storageof information, such as computer readable instructions, data structures,program modules, or other data. System memory 704, removable storage 716and non-removable storage 718 are all examples of computer storagemedia. Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by Computing device 700. Any such computerstorage media may be part of device 700. Computing device 700 may alsohave input device(s) 720 such as keyboard, mouse, pen, voice inputdevice, touch input device, etc. Output device(s) 722 such as a display,speakers, printer, etc. may also be included.

Computing device 700 may also contain communication connections 724 thatallow the device to communicate with other computing devices 726, suchas over a network. These networks may include wired networks as well aswireless networks. Communication connections 724 are some examples ofcommunication media. Communication media may typically be embodied bycomputer readable instructions, data structures, program modules, etc.

It is appreciated that the illustrated computing device 700 is only oneexample of a suitable device and is not intended to suggest anylimitation as to the scope of use or functionality of the variousembodiments described. Other well-known computing devices, systems,environments and/or configurations that may be suitable for use with theembodiments include, but are not limited to personal computers, servercomputers, hand-held or laptop devices, multiprocessor systems,microprocessor-base systems, set top boxes, game consoles, programmableconsumer electronics, network PCs, minicomputers, mainframe computers,distributed computing environments that include any of the above systemsor devices, and/or the like.

The implementation of automated performance prediction for cloudservices on a client device may enable the assessment of cloud serviceperformance variation in response to a wide range of hypotheticalscenarios. Thus, the techniques described herein may enable theprediction of parameter settings that provide optimal cloud serviceperformance, i.e., shortest page load time (PLT) without error proneand/or time consuming manual trial-and-error parameter modifications andperformance assessments. Moreover, the implementation of automatedperformance prediction for cloud services on a client device may takeinto account factors that are not visible to cloud service providers.These factors may include page rendering time, object dependencies,multiple data sources across data centers and data providers.

CONCLUSION

In closing, although the various embodiments have been described inlanguage specific to structural features and/or methodological acts, itis to be understood that the subject matter defined in the appendedrepresentations is not necessarily limited to the specific features oracts described. Rather, the specific features and acts are disclosed asexemplary forms of implementing the claimed subject matter.

1. A system, comprising: one or more processors; a memory that includesa plurality of computer-executable components, the plurality ofcomputer-executable components comprising: a dependency extractor thatextracts one or more dependency relationships for each web object in awebpage, each dependency relationship being a relationship between acorresponding web object and an associated descendant web object or anassociated ancestor web object; a performance predictor that ascertainsan original performance metric value associated with a page loading ofthe webpage, and simulates an additional page loading of the webpagebased on adjusted timing information and dependency relationshipsbetween the web objects in the webpage to estimate a new performancemetric value associated with the additional page loading; and acomparison engine that determines whether the adjusted timinginformation increased or decreased the new performance metric value ascompared to the original performance metric value.
 2. The system ofclaim 1, further comprising an automated web robot that drives a webbrowser to perform the page loading of the webpage.
 3. The system ofclaim 1, wherein the adjusted timing information differs from originaltiming information associated with the page loading of the webpage. 4.The system of claim 3, further comprising a measurement engine thatrecords the original timing information associated with the page loadingof the webpage.
 5. The system of claim 3, wherein the original timinginformation includes at least one of a client delay associated with atleast one of page rendering or JavaScript execution of the webpage, aserver delay associated with at least one of retrieving static contentor generating dynamic content of the webpage, a network delay associatedwith loading a web object of the webpage, or a round trip time (RTT)associated with loading the web object of the webpage.
 6. The system ofclaim 1, wherein the adjusted timing information includes at least oneof a modified client delay associated with at least one of pagerendering or JavaScript execution of the webpage, a modified serverdelay associated with at least one of retrieving static content orgenerating dynamic content of the webpage, a modified network delayassociated with loading a web object of the webpage, or a modified RTTassociated with loading the web object of the webpage.
 7. The system ofclaim 6, wherein the modified network delay includes at least one of amodified domain name system (DNS) lookup time, a modified transmissioncontrol protocol (TCP) handshake time, or a modified data transfer timeassociated with loading the web object of the webpage.
 8. The system ofclaim 1, wherein the dependency extractor discovers a dependencyrelationship between a first web object and a second web object of thewebpage based at least on a delay in downloading of the first web objectdelaying a download of the second web object.
 9. The system of claim 1,wherein the dependency extractor encapsulates the dependencyrelationships between the web objects in the webpage in a parentaldependency graph (PDG).
 10. The system of claim 1, wherein the originalperformance metric value includes an original page load time for thewebpage, and the new performance metric value includes a new page loadtime for the webpage.
 11. The system of claim 1, wherein the webpageincludes at least one of a JavaScript object, a cascading style sheet(CSS) object, a hypertext markup language (HTML) object, or an imageobject.
 12. A computer readable memory storing computer-executableinstructions that, when executed, cause one or more processors toperform acts comprising: extracting one or more dependency relationshipsfor each web object in a webpage, each dependency relationship being arelationship between a corresponding web object and an associateddescendant web object or an associated ancestor web object; ascertainingan original performance metric value associated with a page loading ofthe webpage; modifying original timing information associated with thepage loading of the webpage to obtain adjusted timing information;simulating an additional page loading of the webpage based on theadjusted timing information and dependency relationships between the webobjects in the webpage to estimate a new performance metric valueassociated with the additional page loading; and implementing theadjusted timing information for executing a subsequent page loading ofthe webpage in response to determining that the new performance metricvalue is less than the original performance metric value.
 13. Thecomputer readable memory of claim 12, wherein the original performancemetric value includes an original page load time for the webpage, andthe new performance metric value includes a new page load time for thewebpage.
 14. The computer readable memory of claim 12, wherein theoriginal timing information includes at least one of a client delayassociated with at least one of page rendering or JavaScript executionof the webpage, a server delay associated with at least one ofretrieving static content or generating dynamic content of the webpage,a network delay associated with loading a web object of the webpage, ora round trip time (RTT) associated with loading the web object of thewebpage.
 15. The computer readable memory of claim 12, wherein theextracting includes encapsulating the dependency relationships betweenthe web objects in the webpage in a parental dependency graph (PDG). 16.The computer readable memory of claim 12, wherein the extractingincludes discovering a dependency relationship between a first webobject and a second web object of the webpage based at least on a delayin downloading of the first web object delaying a download of the secondweb object.
 17. The computer readable memory of claim 12, wherein themodifying the original timing information includes at least one ofmodifying a client delay associated with at least one of page renderingor JavaScript execution of the webpage, modifying a server delayassociated with at least one of retrieving static content or generatingdynamic content of the webpage, modifying a network delay associatedwith loading a web object of the webpage, or modifying a RTT associatedwith loading the web object of the webpage.
 18. The computer readablememory of claim 17, wherein the modifying the network delay includes atleast one of modifying a domain name system (DNS) lookup time, modifyinga transmission control protocol (TCP) handshake time, or modifying adata transfer time associated with loading the web object of thewebpage.
 19. A computer-implemented method, comprising: extracting oneor more dependency relationships for each web object in a webpage, eachdependency relationship being a relationship between a corresponding webobject and an associated descendant web object or an associated ancestorweb object; ascertaining an original performance metric value associatedwith a page loading of the webpage; modifying original timinginformation associated with the page loading of the webpage to obtainadjusted timing information; simulating an additional page loading ofthe webpage based on the adjusted timing information and dependencyrelationships between the web objects in the webpage to estimate a newperformance metric value associated with the additional page loading;and determining whether the adjusted timing information increased ordecreased the new performance metric value as compared to the originalperformance metric value.
 20. The computer-implemented method of claim19, wherein the extracting includes encapsulating the dependencyrelationships between the web objects in the webpage in a parentaldependency graph (PDG), and wherein the original performance metricvalue include an original page load time for the webpage, and the newperformance metric value includes a new page load time for the webpage.